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Introduction to fire danger rating and remote sensing - Will remote sensing enhance wildland fire danger prediction?

While ‘Fire Danger’ per se cannot be measured, the physical properties of the biotic and abiotic world that relate to fire occurrence and fire behavior can. Today, increasingly sophisticated Remote Sensing methods are being developed to more accurately detect fuel properties such as species composition (fuel types), vegetation structure or plant water content - to name a few. Based on meteorological input data and physical, semi-physical or empirical model calculations, Wildland Fire Danger Rating Systems provide ‘indirect values’ - numerical indices - at different temporal scales (e.g., daily, weekly, monthly) denoting the physical conditions that may lead to fire ignition and support fire propagation. The results can be expressed as fire danger levels, ranging from ‘low’ to ‘very high’, and are commonly used in operational wildland fire management (e.g., the Canadian Fire Weather Index [FWI] System, the Russian Nesterov Index, or the U.S. National Fire Danger Rating System [NFDRS]). Today, fire danger levels are often turned into broad scale maps with the help of Geographical Information Systems (GIS) showing the areas with the different fire danger levels, and are distributed via the World Wide Web. In this chapter we will outline some key issues dealing with Remote Sensing and GIS techniques that are covered in the following chapters, and elaborate how the Fire Danger Rating concepts could be integrated into a framework that enables comprehensive and sustainable wildland fire risk assessment. To do so, we will first raise some general thoughts about wildland fires and suggest how to approach this extremely complex phenomenon. Second, we will outline a possible fire risk analysis framework and third we will give a short overview on existing Fire Danger Rating Systems and the principles behind them.

Series in Remote Sensing↗

What is a picture worth? A history of remote sensing

Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m. Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.

Hydrological Sciences Bulletin↗

Ground-water applications of remote sensing

Remote sensing can be used as a tool to inventory springs and seeps and to interpret lithology, structure, and ground-water occurrence and quality. Thermograms are the best images for inventory of seeps and springs. The steps in aquifer mapping are image analysis and interpretation and ground-water interpretation. A ground-water interpretation is derived from a conceptual geologic model by inferring aquifer characteristics and water salinity. The image selection process is very important for obtaining maximum geologic and hydrologic information from remotely sensed data. Remote sensing can contribute an image base map or geologic and hydrologic parameters, derived from the image, to the multiple data sets in a hydrologic information system. Various merging and integration techniques may then be used to obtain information from these data sets.

Open-File Report↗

Remote sensing of global croplands for food security: Way forward

This book opens a new pathway for global mapping that is focused on a specific land use theme, such as irrigated or rain-fed croplands and classes within these themes. Since croplands use most of the water consumed by humans, specific knowledge of irrigated and rain-fed croplands will be critical for precise estimates of water use. At present and in the coming decades, irrigated and rain-fed cropland area mapping is crucial for food security studies. Throughout this book, various subjects pertaining to global croplands are discussed comprehensively.

Book chapter↗

Remote sensing of soil moisture using airborne hyperspectral data

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation ( R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

GIScience and Remote Sensing↗

Advanced Remote Sensing Research

'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).

Fact Sheet↗

Restoration monitoring metric framework: Integrating innovative remote-sensing technologies: Comparisons between field and remotely sensed vegetation surveys of restored forested and grassland sites in Ohio

Restoration monitoring is generally perceived as costly and time-consuming, yet the concept of universal restoration monitoring metrics is trending for evaluation of restoration performance across spatial scales, project boundaries, and jurisdictions. Natural Resource Damage Assessment and Restoration (NRDAR) practitioners seek to restore natural resources injured by oil spills or hazardous substance releases into the environment. Therefore, a multiagency team [US Army Engineer Research and Development Center (ERDC), US Department of the Interior (DOI), and US Department of Energy (DOE)] developed and field-tested a multitiered monitoring framework, illustrating a range of field and remote-sensing techniques and methodologies. The restoration monitoring framework and field demonstration offer a unique methodology to acquire and evaluate simultaneously collected, multiscale/multiplatform data. The result of this research provides new insights to (1) assist planning, implementing, and monitoring restoration progress and effectiveness; and (2) apply common monitoring methods, endpoints, and metrics to other types of ecosystem restoration initiatives. Although the aim was to inform monitoring and management of areas that had been injured, these methods could also be used to inform restoration monitoring practices in a broader context, benefiting environmental stewardship missions of all project partners.

Final Technical Report↗

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

Remote Sensing↗

Remote sensing and photogrammetric studies: Part A: remote sensing of Mare Serenitatis

Mare Serenitatis is a circular mare approximately 600 km in diameter in the northeast quadrant of the lunar near side. It occupies an old multi-ringed basin (refs. 33-1 and 33-2) and is the site of a prominent mascon (ref. 33-3). A conspicuous dark annulus in this mare prompted subdivision of the mare materials into different stratigraphic units (refs. 33-2 and 33-4). A revised stratigraphic sequence for the southern part of Mare Serenitatis, based on photogeologic interpretation of Apollo 15 and 17 photographs, is summarized as follows after Howard et al.

Book chapter↗

Analysis of potential debris flow source areas on Mount Shasta, California, by using airborne and satellite remote sensing data

Remote sensing data from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the first spaceborne imaging spectrometer, Hyperion, show hydrothermally altered rocks mainly composed of natroalunite, kaolinite, cristobalite, and gypsum on both the Mount Shasta and Shastina cones. Field observations indicate that much of the visible altered rock consists of talus material derived from fractured rock zones within and adjacent to dacitic domes and nearby lava flows. Digital elevation data were utilized to distinguish steeply sloping altered bedrock from more gently sloping talus materials. Volume modeling based on the imagery and digital elevation data indicate that Mount Shasta drainage systems contain moderate volumes of altered rock, a result that is consistent with Mount Shasta's Holocene record of mostly small to moderate debris flows. Similar modeling for selected areas at Mount Rainier and Mount Adams, Washington, indicates larger altered rock volumes consistent with the occurrence of much larger Holocene debris flows at those volcanoes. The availability of digital elevation and spectral data from spaceborne sensors, such as Hyperion and the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), greatly expands opportunities for studying potential debris flow source characteristics at stratovolcanoes around the world. ?? 2003 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗

Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations

This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m 3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points).

Journal of Applied Remote Sensing↗

The future of coastal monitoring through satellite remote sensing

Satellite remote sensing is transforming coastal science from a “data-poor” field into a “data-rich” field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change have outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, that is, (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer vision to fully leverage this massive treasure trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earth-observing satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems.

Cambridge Prisms: Coastal Futures↗

Geologic remote sensing

Remote-sensing techniques are now being used routinely in geologic interpretation for mineral and energy exploration, plant siting, waste disposal, and the development of models for regional and continental tectonics. New spaceborne methods and associated technologies are being developed to produce data from which geologic information about large areas can be derived much more rapidly than by conventional techniques.

Science↗

Software application for spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A tool for identifying cyanobacteria genera from remotely sensed data

Remote sensing is often used to detect algae, but standard techniques do not provide information on the types of algae present or their potential to form a harmful algal bloom (HAB). We developed a framework for identifying algal genera based on reflectance: SMASH, short for Spectral Mixture Analysis for Surveillance of HABs. The Software Application for SMASH (SAS) was developed in MATLAB and makes use of a Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm implemented in Python but packaged as a standalone executable. SAS includes functions for importing hyperspectral images, resampling spectral libraries, evaluating endmember spectral separability, performing MESMA, and generating various output data products.

Journal of Open Research Software (JORS)↗

Lunar remote sensing and measurements

Remote sensing and measurements of the Moon from Apollo orbiting spacecraft and Earth form a basis for extrapolation of Apollo surface data to regions of the Moon where manned and unmanned spacecraft have not been and may be used to discover target regions for future lunar exploration which will produce the highest scientific yields. Orbital remote sensing and measurements discussed include (1) relative ages and inferred absolute ages, (2) gravity, (3) magnetism, (4) chemical composition, and (5) reflection of radar waves (bistatic). Earth-based remote sensing and measurements discussed include (1) reflection of sunlight, (2) reflection and scattering of radar waves, and (3) infrared eclipse temperatures. Photographs from the Apollo missions, Lunar Orbiters, and other sources provide a fundamental source of data on the geology and topography of the Moon and a basis for comparing, correlating, and testing the remote sensing and measurements. Relative ages obtained from crater statistics and then empirically correlated with absolute ages indicate that significant lunar volcanism continued to 2.5 b.y. (billion years) ago-some 600 m.y. (million years) after the youngest volcanic rocks sampled by Apollo-and that intensive bombardment of the Moon occurred in the interval of 3.84 to 3.9 b.y. ago. Estimated fluxes of crater-producing objects during the last 50 m.y. agree fairly well with fluxes measured by the Apollo passive seismic stations. Gravity measurements obtained by observing orbiting spacecraft reveal that mare basins have mass concentrations and that the volume of material ejected from the Orientale basin is near 2 to 5 million km 3 depending on whether there has or has not been isostatic compensation, little or none of which has occurred since 3.84 b.y. ago. Isostatic compensation may have occurred in some of the old large lunar basins, but more data are needed to prove it. Steady fields of remanent magnetism were detected by the Apollo 15 and 16 subsatellites, and the lunar dipole field was revised to no more than 6x 10 19 gauss. High-resolution mapping of fields of weak remanent magnetism (to 0.1 gamma) was made possible by the Apollo plasma and energetic-particle experiment. Although the causes of remanent magnetism are poorly understood, correlations with geologic units suggest the results may ultimately have farreaching significance to lunar history. Maria are much less structured by strong surface magnetic anomalies than the highlands. The strongest anomalies are associated with ejecta of farside basins, plains materials filling pre-Imbrian craters, and other old Imbrian to pre-Imbrian units. The high remanent fields could be due to cooling of ejecta units in an ancient magnetic field, lunar regolith maturity, extensive reworking and disruption of a magnetized layer, or simply surface roughness. Orbital geochemical experiments have shown that lunar high lands have larger Al: Si ratios and smaller Mg: Si ratios than maria. These two ratios are inversely related on a regional basis. With the exception of fresh craters, albedo and Al : Si ratios vary directly, showing that compositional differences as well as exposure of fresh materials are responsible for high albedos. Statistically treated data show that geologic contacts and compositional boundaries are concentric and can be roughly matched. Some craters on mare material have penetrated the mare fill, bringing highland-type materials to the surface. Natural radioactivity from thorium, potassium, and uranium is inversely correlated with elevation. Mare regions are enriched in iron, titanium, and magnesium relative to the highlands. Orbital bistatic-radar results provide estimates of surface roughness at two scale lengths (about 30 m and 250 m), which agree with visual estimates of roughness. The dielectric constant of the lunar surface, where sampled, is uniform to 13-cm radar and near 3. Slope frequency distributions measured by the radar vary and

Professional Paper↗

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km 2 study area in Arctic Alaska. The study area is centered at 69 °N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000–2009.

Alaska↗

Advanced and applied remote sensing of environmental conditions

"Remote sensing” is a general term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth’s surface. Remotely sensed data are fundamental to geographic science. The U.S. Geological Survey’s (USGS) Eastern Geographic Science Center (EGSC) is currently conducting and promoting the research and development of several different aspects of remote sensing science in both the laboratory and from overhead instruments. Spectroscopy is the science of recording interactions of energy and matter and is the bench science for all remote sensing. Visible and infrared analysis in the laboratory with special instruments called spectrometers enables the transfer of this research from the laboratory to multispectral (5–15 broad bands) and hyperspectral (50–300 narrow contiguous bands) analyses from aircraft and satellite sensors. In addition, mid-wave (3–5 micrometers, µm) and long-wave (8–14 µm) infrared data analysis, such as attenuated total reflectance (ATR) spectral analysis, are also conducted. ATR is a special form of vibrational infrared spectroscopy that has many applications in chemistry and biology but has recently been shown to be especially diagnostic for vegetation analysis.

Fact Sheet↗

Some guidelines for remote sensing in hydrology

Remote sensing in the field of hydrology is beginning to be applied to significant problems, such as thermal pollution, in many programs of the Federal and State Governments as well as in operation of many private organizations. The purpose of this paper is to guide the hydrologist to a better understanding of how he may collect, synthesize, and interpret remote sensing data.

Journal of the American Water Resources Associatio↗